Automatic spelling correction has been receiving sustained research attention. Although each article contains a brief introduction to the topic, there is a lack of work that would summarize the theoretical framework and provide an overview of the approaches developed so far. Our survey selected papers about spelling correction indexed in Scopus and Web of Science from 1991 to 2019. The first group uses a set of rules designed in advance. The second group uses an additional model of context. The third group of automatic spelling correction systems in the survey can adapt its model to the given problem. The summary tables show the application area, language, string metrics, and context model for each system. The survey describes selected approaches in a common theoretical framework based on Shannon’s noisy channel. A separate section describes evaluation methods and benchmarks.

SK-QuAD is the first manually annotated dataset of questions and answers in Slovak. It consists of more than 91k factual questions and answers from various fields. Each question has an answer marked in the corresponding paragraph. It also contains negative examples in the form of "unanswered questions" and "plausible answers". The dataset is published free of charge for scientific use. We aim to contribute to the creation of Slovak or multilingual systems for generating an answer to a question in a natural language. The paper provides an overview of the existing datasets for question answering. It describes the annotation process and statistically analyzes the created content. The dataset expands the possibilities of training and evaluation of multilingual language models. Experiments show that the dataset achieves state-of-the-art results for Slovak and improves question answering for other languages in zero-shot learning. We compare the effect of machine-translated data with manually annotated. Additional data improve the modeling for low-resourced languages.

Large databases of scanned documents (medical records, legal texts, historical documents) require natural language processing for retrieval and structured information extraction. Errors caused by the optical character recognition (OCR) system increase ambiguity of recognized text and decrease performance of natural language processing. The paper proposes OCR post correction system with parametrized string distance metric. The correction system learns specific error patterns from incorrect words and common sequences of correct words. A smoothing technique is proposed to assign non-zero probability to edit operations not present in the training corpus. Spelling correction accuracy is measured on database of OCR legal documents in English language. Language model and learning string metric with smoothing improves Viterbi-based search for the best sequence of corrections and increases performance of the spelling correction system.

This paper presents a comprehensive evaluation and comparison of selected conventional and advanced approaches for detecting and classifying emotional stress levels from speech. Three representative and publicly available stress datasets – CRISIS, StressDat, and SUSAS – were utilized. A detailed comparative analysis of their speech characteristics, including fundamental frequency (F₀), formant frequencies (F₁, F₂, F₃), voice intensity, harmonic-to-noise ratio, jitter, and shimmer, was conducted to investigate how these features change with increasing stress and contribute to its detection. Both conventional feature-based methods and deep learning techniques, including transfer and self-supervised learning, are explored in the experimental part of this research. The best classification results among the conventional methods were achieved using a combination of features extracted via the Discrete Wavelet Transform and GammaTone Cepstral Coefficients (DWT+GTCC), paired with a Subspace k-Nearest Neighbors classifier, which yielded F1-scores of up to 91.1% on CRISIS and 82.4% on StressDat. Among the deep learning approaches, we fine-tuned self-supervised models such as Wav2Vec 2.0 and BYOL-S (Bootstrap Your Own Latent for Speech), as well as transfer learning models including VGGish and YAMNet.Wav2Vec 2.0 consistently outperformed the others, achieving an F1-score of 94% on CRISIS and over 87% on StressDat. On the SUSAS dataset, which contains speech under simulated and real stress conditions, an F1-score of 77% was achieved using both the fine-tuned Wav2Vec 2.0 model as well as a pipeline combining BYOL-S feature extraction with a Support Vector Machine (SVM) classifier. After merging all stress-related datasets into a single balanced dataset,Wav2Vec 2.0 achieved the highest F1-score of 85%. Incorporating additional emotional speech data improved robustness and led to a slight increase in the accuracy of classifying stress into three distinct levels. These results highlight the importance of robust feature selection, appropriate classifier design, and the integration of emotional speech data to enhance generalization performance in real-world applications.

The robustness of n-gram language models depends on the quality of text data on which they have been trained. The text corpora collected from various resources such as web pages or electronic documents are characterized by many possible topics. In order to build efficient and robust domain-specific language models, it is necessary to separate domain-oriented segments from the large amount of text data, and the remaining out-of-domain data can be used only for updating of existing in-domain n-gram probability estimates. In this paper, we describe the process of classification of heterogeneous text data into two classes, to the in-domain and out-of-domain data, mainly used for language modeling in the task-oriented speech recognition from judicial domain. The proposed algorithm for text classification is based on detection of theme in short text segments based on the most frequent key phrases. In the next step, each text segment is represented in vector space model as a feature vector with term weighting. For classification of these text segments to the in-domain and out-of domain area, document similarity with automatic thresholding are used. The experimental results of modeling the Slovak language and adaptation to the judicial domain show significant improvement in the model perplexity and increasing the performance of the Slovak transcription and dictation system.

The Slovak NLP Demo should provide overview of the state-of-the-art NLP tools where each single processing step can be tried with arbitrary text, results can be reviewed and feedback can be sent to the authors. The demo is accessible on a web site, arbitrary text can be inserted using a form. Processing tools can be selected and processed text will be displayed. The web site contains additional resources, such as datasets, other tools and bibliographical information.